MOFA
Mafe Senosain
2020-11-19
Load MOFA models
MOFA model + xCell + VIPER 25
plotDataOverview(MOFA_m_x25)
Analyze trained MOFA model
# Calculate the variance explained (R2) per factor in each view
r2 <- calculateVarianceExplained(MOFA_m_x25)
r2$R2Total## CyTOF_exp CyTOF_prcnt RNA_topgenes RNA_viper25 RNA_xcell mutation
## 0.11657600 0.06723003 0.43927647 0.68715897 0.37877534 0.05456125# Variance explained by each factor in each view
head(r2$R2PerFactor)## CyTOF_exp CyTOF_prcnt RNA_topgenes RNA_viper25 RNA_xcell mutation
## LF1 0.0118403200 0.015294301 0.09931202 0.14660778 0.090561784 5.395057e-02
## LF2 0.0002113100 0.001318306 0.09359987 0.15741472 0.112534474 8.975259e-05
## LF3 0.0001264806 0.005575375 0.05099167 0.08656500 0.093494019 4.292479e-05
## LF4 0.0883613034 0.026199139 0.04140564 0.02766502 0.005864836 5.203288e-05
## LF5 0.0003591671 0.003908529 0.02327361 0.10864514 0.025851111 8.328968e-05
## LF6 0.0161619776 0.013081427 0.01709465 0.05864921 0.017377530 2.675858e-05# Plot it
plotVarianceExplained(MOFA_m_x25)
Characterization of individual factors
CyTOF percentages
plotWeightsHeatmap(
MOFA_m_x25,
view = "CyTOF_prcnt",
factors = 1:5,
show_colnames = T, main = 'CyTOF_prcnt'
)
plotTopWeights(
MOFA_m_x25,
view="CyTOF_prcnt",
factor=5
)
plotTopWeights(
MOFA_m_x25,
view="CyTOF_prcnt",
factor=4
)
plotTopWeights(
MOFA_m_x25,
view="CyTOF_prcnt",
factor=2
)
CyTOF expression
plotWeightsHeatmap(
MOFA_m_x25,
view = "CyTOF_exp",
factors = 1:5,
show_colnames = FALSE, main = 'CyTOF_exp'
)
plotTopWeights(
MOFA_m_x25,
view="CyTOF_exp",
factor=3
)
plotTopWeights(
MOFA_m_x25,
view="CyTOF_exp",
factor=5
)
plotTopWeights(
MOFA_m_x25,
view="CyTOF_exp",
factor=1
)
plotTopWeights(
MOFA_m_x25,
view="CyTOF_exp",
factor=2
)
RNA Seq Top genes
plotWeightsHeatmap(
MOFA_m_x25,
view = "RNA_topgenes",
factors = 1:5,
show_colnames = FALSE, main = 'RNA_topgenes'
)
plotTopWeights(
MOFA_m_x25,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_x25,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_x25,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_x25,
view="RNA_topgenes",
factor=1
)
RNA Seq Deconvolution
plotWeightsHeatmap(
MOFA_m_x25,
view = "RNA_xcell",
factors = 1:5,
show_colnames = T, main = 'RNA_xcell'
)
plotTopWeights(
MOFA_m_x25,
view="RNA_xcell",
factor=2
)
plotTopWeights(
MOFA_m_x25,
view="RNA_xcell",
factor=1
)
plotTopWeights(
MOFA_m_x25,
view="RNA_xcell",
factor=4
)
plotTopWeights(
MOFA_m_x25,
view="RNA_xcell",
factor=5
)
WES
plotWeightsHeatmap(
MOFA_m_x25,
view = "mutation",
factors = 1:5,
show_colnames = F, main = 'Mutation Data'
)
plotTopWeights(
MOFA_m_x25,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_x25,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_x25,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_x25,
view="mutation",
factor=1
)
Feature set enrichment analysis in the active views
# Load reactome annotations
data("reactomeGS") # binary matrix with feature sets in rows and features in columns
# perform enrichment analysis
gsea <- runEnrichmentAnalysis(
MOFA_m_x25,
view = "RNA_topgenes",
feature.sets = reactomeGS,
alpha = 0.01
)## Doing Feature Set Enrichment Analysis with the following options...## View: RNA_topgenes## Factors: LF1 LF2 LF3 LF4 LF5 LF6 LF7 LF8 LF9 LF10## Number of feature sets: 640## Local statistic: loading## Transformation: abs.value## Global statistic: mean.diff## Statistical test: parametricplotEnrichmentBars(gsea, alpha=0.01)
interestingFactors <- 1:2
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_x25,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
interestingFactors <- 3:4
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_x25,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
interestingFactors <- 5:6
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_x25,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
plotFactorScatter(
MOFA_m_x25,
factors = 1:2,
color_by = "ImmuneScore" # color by the IGHV values that are part of the training data
#shape_by = "trisomy12" # shape by the trisomy12 values that are part of the training data
)
plotFactorScatters(
MOFA_m_x25,
factors = c(1:5),
color_by = "ImmuneScore"
)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
plotFactorScatters(
MOFA_m_x25,
factors = c(1:5),
color_by = "Th_cells"
)
plotFactorScatters(
MOFA_m_x25,
factors = c(1:5),
color_by = "Tc_cells"
)
plotFactorScatters(
MOFA_m_x25,
factors = c(1:5),
color_by = "KRAS"
)
plotFactorScatters(
MOFA_m_x25,
factors = c(1:5),
color_by = "EGFR"
)
MOFA model + xCell + VIPER 4
plotDataOverview(MOFA_m_x4)
Analyze trained MOFA model
# Calculate the variance explained (R2) per factor in each view
r2 <- calculateVarianceExplained(MOFA_m_x4)
r2$R2Total## CyTOF_exp CyTOF_prcnt RNA_topgenes RNA_viper4 RNA_xcell mutation
## 0.11576769 0.06624335 0.43926083 0.57566158 0.37905682 0.05759554# Variance explained by each factor in each view
head(r2$R2PerFactor)## CyTOF_exp CyTOF_prcnt RNA_topgenes RNA_viper4 RNA_xcell mutation
## LF1 0.0113030430 0.015172115 0.10020041 0.12228920 0.088427725 5.701099e-02
## LF2 0.0001943663 0.001282022 0.09267818 0.13254738 0.114530255 7.739652e-05
## LF3 0.0001225166 0.005556197 0.05100182 0.07772323 0.094508949 4.457243e-05
## LF4 0.0883818991 0.026203092 0.04135978 0.01985176 0.005879997 5.094061e-05
## LF5 0.0002796930 0.003733890 0.02359641 0.07918044 0.028225011 8.407998e-05
## LF6 0.0159356064 0.012416670 0.01708104 0.03951246 0.017624428 2.669282e-05# Plot it
plotVarianceExplained(MOFA_m_x4)
Characterization of individual factors
CyTOF percentages
plotWeightsHeatmap(
MOFA_m_x4,
view = "CyTOF_prcnt",
factors = 1:5,
show_colnames = T, main = 'CyTOF_prcnt'
)
plotTopWeights(
MOFA_m_x4,
view="CyTOF_prcnt",
factor=5
)
plotTopWeights(
MOFA_m_x4,
view="CyTOF_prcnt",
factor=4
)
plotTopWeights(
MOFA_m_x4,
view="CyTOF_prcnt",
factor=2
)
CyTOF expression
plotWeightsHeatmap(
MOFA_m_x4,
view = "CyTOF_exp",
factors = 1:5,
show_colnames = FALSE, main = 'CyTOF_exp'
)
plotTopWeights(
MOFA_m_x4,
view="CyTOF_exp",
factor=3
)
plotTopWeights(
MOFA_m_x4,
view="CyTOF_exp",
factor=5
)
plotTopWeights(
MOFA_m_x4,
view="CyTOF_exp",
factor=1
)
plotTopWeights(
MOFA_m_x4,
view="CyTOF_exp",
factor=2
)
RNA Seq Top genes
plotWeightsHeatmap(
MOFA_m_x4,
view = "RNA_topgenes",
factors = 1:5,
show_colnames = FALSE, main = 'RNA_topgenes'
)
plotTopWeights(
MOFA_m_x4,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_x4,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_x4,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_x4,
view="RNA_topgenes",
factor=1
)
RNA Seq Deconvolution
plotWeightsHeatmap(
MOFA_m_x4,
view = "RNA_xcell",
factors = 1:5,
show_colnames = T, main = 'RNA_xcell'
)
plotTopWeights(
MOFA_m_x4,
view="RNA_xcell",
factor=2
)
plotTopWeights(
MOFA_m_x4,
view="RNA_xcell",
factor=1
)
plotTopWeights(
MOFA_m_x4,
view="RNA_xcell",
factor=4
)
plotTopWeights(
MOFA_m_x4,
view="RNA_xcell",
factor=5
)
WES
plotWeightsHeatmap(
MOFA_m_x4,
view = "mutation",
factors = 1:5,
show_colnames = F, main = 'Mutation Data'
)
plotTopWeights(
MOFA_m_x4,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_x4,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_x4,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_x4,
view="mutation",
factor=1
)
Feature set enrichment analysis in the active views
# Load reactome annotations
data("reactomeGS") # binary matrix with feature sets in rows and features in columns
# perform enrichment analysis
gsea <- runEnrichmentAnalysis(
MOFA_m_x4,
view = "RNA_topgenes",
feature.sets = reactomeGS,
alpha = 0.01
)## Doing Feature Set Enrichment Analysis with the following options...## View: RNA_topgenes## Factors: LF1 LF2 LF3 LF4 LF5 LF6 LF7 LF8 LF9 LF10## Number of feature sets: 640## Local statistic: loading## Transformation: abs.value## Global statistic: mean.diff## Statistical test: parametricplotEnrichmentBars(gsea, alpha=0.01)
interestingFactors <- 1:2
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_x4,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
interestingFactors <- 3:4
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_x4,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
interestingFactors <- 5:6
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_x4,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
plotFactorScatter(
MOFA_m_x4,
factors = 1:2,
color_by = "ImmuneScore" # color by the IGHV values that are part of the training data
#shape_by = "trisomy12" # shape by the trisomy12 values that are part of the training data
)
plotFactorScatters(
MOFA_m_x4,
factors = c(1:5),
color_by = "ImmuneScore"
)
plotFactorScatters(
MOFA_m_x4,
factors = c(1:5),
color_by = "Th_cells"
)
plotFactorScatters(
MOFA_m_x4,
factors = c(1:5),
color_by = "Tc_cells"
)
plotFactorScatters(
MOFA_m_x4,
factors = c(1:5),
color_by = "KRAS"
)
plotFactorScatters(
MOFA_m_x4,
factors = c(1:5),
color_by = "EGFR"
)
MOFA model + EpiDISH + VIPER 25
plotDataOverview(MOFA_m_e25)
Analyze trained MOFA model
# Calculate the variance explained (R2) per factor in each view
r2 <- calculateVarianceExplained(MOFA_m_e25)
r2$R2Total## CyTOF_exp CyTOF_prcnt RNA_epidish RNA_topgenes RNA_viper25 mutation
## 0.11553712 0.06503805 0.37666450 0.43951638 0.68737716 0.06443597# Variance explained by each factor in each view
head(r2$R2PerFactor)## CyTOF_exp CyTOF_prcnt RNA_epidish RNA_topgenes RNA_viper25 mutation
## LF1 0.0001343032 0.005526604 2.236585e-01 0.05150576 0.08653719 4.485793e-05
## LF2 0.0101933986 0.013242724 2.312529e-02 0.10309008 0.13888167 6.393188e-02
## LF3 0.0001835709 0.001488031 6.053233e-02 0.08948466 0.16638152 4.904507e-05
## LF4 0.0885562816 0.026066946 6.011119e-05 0.04137867 0.02738389 4.820525e-05
## LF5 0.0006020680 0.004083677 2.151768e-03 0.02301456 0.10351271 7.980884e-05
## LF6 0.0164341056 0.012762354 2.790510e-02 0.01710937 0.05868396 2.697200e-05# Plot it
plotVarianceExplained(MOFA_m_e25)
Characterization of individual factors
CyTOF percentages
plotWeightsHeatmap(
MOFA_m_e25,
view = "CyTOF_prcnt",
factors = 1:5,
show_colnames = T, main = 'CyTOF_prcnt'
)
plotTopWeights(
MOFA_m_e25,
view="CyTOF_prcnt",
factor=5
)
plotTopWeights(
MOFA_m_e25,
view="CyTOF_prcnt",
factor=4
)
plotTopWeights(
MOFA_m_e25,
view="CyTOF_prcnt",
factor=2
)
CyTOF expression
plotWeightsHeatmap(
MOFA_m_e25,
view = "CyTOF_exp",
factors = 1:5,
show_colnames = FALSE, main = 'CyTOF_exp'
)
plotTopWeights(
MOFA_m_e25,
view="CyTOF_exp",
factor=3
)
plotTopWeights(
MOFA_m_e25,
view="CyTOF_exp",
factor=5
)
plotTopWeights(
MOFA_m_e25,
view="CyTOF_exp",
factor=1
)
plotTopWeights(
MOFA_m_e25,
view="CyTOF_exp",
factor=2
)
RNA Seq Top genes
plotWeightsHeatmap(
MOFA_m_e25,
view = "RNA_topgenes",
factors = 1:5,
show_colnames = FALSE, main = 'RNA_topgenes'
)
plotTopWeights(
MOFA_m_e25,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_e25,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_e25,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_e25,
view="RNA_topgenes",
factor=1
)
RNA Seq Deconvolution
plotWeightsHeatmap(
MOFA_m_e25,
view = "RNA_epidish",
factors = 1:5,
show_colnames = T, main = 'RNA_epidish'
)
plotTopWeights(
MOFA_m_e25,
view="RNA_epidish",
factor=2
)
plotTopWeights(
MOFA_m_e25,
view="RNA_epidish",
factor=1
)
plotTopWeights(
MOFA_m_e25,
view="RNA_epidish",
factor=4
)
plotTopWeights(
MOFA_m_e25,
view="RNA_epidish",
factor=5
)
WES
plotWeightsHeatmap(
MOFA_m_e25,
view = "mutation",
factors = 1:5,
show_colnames = F, main = 'Mutation Data'
)
plotTopWeights(
MOFA_m_e25,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_e25,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_e25,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_e25,
view="mutation",
factor=1
)
Feature set enrichment analysis in the active views
# Load reactome annotations
data("reactomeGS") # binary matrix with feature sets in rows and features in columns
# perform enrichment analysis
gsea <- runEnrichmentAnalysis(
MOFA_m_e25,
view = "RNA_topgenes",
feature.sets = reactomeGS,
alpha = 0.01
)## Doing Feature Set Enrichment Analysis with the following options...## View: RNA_topgenes## Factors: LF1 LF2 LF3 LF4 LF5 LF6 LF7 LF8 LF9 LF10## Number of feature sets: 640## Local statistic: loading## Transformation: abs.value## Global statistic: mean.diff## Statistical test: parametricplotEnrichmentBars(gsea, alpha=0.01)
interestingFactors <- 1:2
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_e25,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
interestingFactors <- 3:4
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_e25,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
interestingFactors <- 5:6
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_e25,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
plotFactorScatters(
MOFA_m_e25,
factors = c(1:5),
color_by = "Th_cells"
)
plotFactorScatters(
MOFA_m_e25,
factors = c(1:5),
color_by = "Tc_cells"
)
plotFactorScatters(
MOFA_m_e25,
factors = c(1:5),
color_by = "KRAS"
)
plotFactorScatters(
MOFA_m_e25,
factors = c(1:5),
color_by = "EGFR"
)
MOFA model + EpiDISH + VIPER 4
plotDataOverview(MOFA_m_e4)
Analyze trained MOFA model
# Calculate the variance explained (R2) per factor in each view
r2 <- calculateVarianceExplained(MOFA_m_e4)
r2$R2Total## CyTOF_exp CyTOF_prcnt RNA_epidish RNA_topgenes RNA_viper4 mutation
## 0.11599445 0.06493035 0.37737421 0.43952482 0.57569168 0.06514388# Variance explained by each factor in each view
head(r2$R2PerFactor)## CyTOF_exp CyTOF_prcnt RNA_epidish RNA_topgenes RNA_viper4 mutation
## LF1 0.0001425716 0.005552850 2.250425e-01 0.05144924 0.07776017 4.480380e-05
## LF2 0.0102215277 0.013063389 2.107351e-02 0.10334330 0.11982511 6.464697e-02
## LF3 0.0001799317 0.001556641 6.213197e-02 0.08928844 0.13553456 4.701630e-05
## LF4 0.0891707850 0.026472316 6.144777e-05 0.04133211 0.01970607 4.651060e-05
## LF5 0.0162206720 0.012200086 2.779923e-02 0.01708670 0.03946763 2.648969e-05
## LF6 0.0006057104 0.004164464 2.448574e-03 0.02308369 0.07156277 8.073406e-05# Plot it
plotVarianceExplained(MOFA_m_e4)
Characterization of individual factors
CyTOF percentages
plotWeightsHeatmap(
MOFA_m_e4,
view = "CyTOF_prcnt",
factors = 1:5,
show_colnames = T, main = 'CyTOF_prcnt'
)
plotTopWeights(
MOFA_m_e4,
view="CyTOF_prcnt",
factor=5
)
plotTopWeights(
MOFA_m_e4,
view="CyTOF_prcnt",
factor=4
)
plotTopWeights(
MOFA_m_e4,
view="CyTOF_prcnt",
factor=2
)
CyTOF expression
plotWeightsHeatmap(
MOFA_m_e4,
view = "CyTOF_exp",
factors = 1:5,
show_colnames = FALSE, main = 'CyTOF_exp'
)
plotTopWeights(
MOFA_m_e4,
view="CyTOF_exp",
factor=3
)
plotTopWeights(
MOFA_m_e4,
view="CyTOF_exp",
factor=5
)
plotTopWeights(
MOFA_m_e4,
view="CyTOF_exp",
factor=1
)
plotTopWeights(
MOFA_m_e4,
view="CyTOF_exp",
factor=2
)
RNA Seq Top genes
plotWeightsHeatmap(
MOFA_m_e4,
view = "RNA_topgenes",
factors = 1:5,
show_colnames = FALSE, main = 'RNA_topgenes'
)
plotTopWeights(
MOFA_m_e4,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_e4,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_e4,
view="RNA_topgenes",
factor=1
)
plotTopWeights(
MOFA_m_e4,
view="RNA_topgenes",
factor=1
)
RNA Seq Deconvolution
plotWeightsHeatmap(
MOFA_m_e4,
view = "RNA_epidish",
factors = 1:5,
show_colnames = T, main = 'RNA_epidish'
)
plotTopWeights(
MOFA_m_e4,
view="RNA_epidish",
factor=2
)
plotTopWeights(
MOFA_m_e4,
view="RNA_epidish",
factor=1
)
plotTopWeights(
MOFA_m_e4,
view="RNA_epidish",
factor=4
)
plotTopWeights(
MOFA_m_e4,
view="RNA_epidish",
factor=5
)
WES
plotWeightsHeatmap(
MOFA_m_e4,
view = "mutation",
factors = 1:5,
show_colnames = F, main = 'Mutation Data'
)
plotTopWeights(
MOFA_m_e4,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_e4,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_e4,
view="mutation",
factor=1
)
plotTopWeights(
MOFA_m_e4,
view="mutation",
factor=1
)
Feature set enrichment analysis in the active views
# Load reactome annotations
data("reactomeGS") # binary matrix with feature sets in rows and features in columns
# perform enrichment analysis
gsea <- runEnrichmentAnalysis(
MOFA_m_e4,
view = "RNA_topgenes",
feature.sets = reactomeGS,
alpha = 0.01
)## Doing Feature Set Enrichment Analysis with the following options...## View: RNA_topgenes## Factors: LF1 LF2 LF3 LF4 LF5 LF6 LF7 LF8 LF9 LF10## Number of feature sets: 640## Local statistic: loading## Transformation: abs.value## Global statistic: mean.diff## Statistical test: parametricplotEnrichmentBars(gsea, alpha=0.01)
interestingFactors <- 1:2
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_e4,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
interestingFactors <- 3:4
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_e4,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
interestingFactors <- 5:6
fseaplots <- lapply(interestingFactors, function(factor) {
plotEnrichment(
MOFA_m_e4,
gsea,
factor = factor,
alpha = 0.01,
max.pathways = 10 # The top number of pathways to display
)
})
cowplot::plot_grid(fseaplots[[1]], fseaplots[[2]],
ncol = 1, labels = paste("Factor", interestingFactors))
plotFactorScatters(
MOFA_m_e4,
factors = c(1:5),
color_by = "Th_cells"
)
plotFactorScatters(
MOFA_m_e4,
factors = c(1:5),
color_by = "Tc_cells"
)
plotFactorScatters(
MOFA_m_e4,
factors = c(1:5),
color_by = "KRAS"
)
plotFactorScatters(
MOFA_m_e4,
factors = c(1:5),
color_by = "EGFR"
)